Efficient SPARQL Query Processing in MapReduce

Existing approaches to parallel SPARQL query processing in MapReduce often suffer from suboptimal parallelism, particularly when handling complex queries with multiple joins. Recent advancements have explored novel multiway join techniques, indexing mechanisms, and distributed graph systems to address these challenges. In this paper, we extend the discussion by incorporating state-of-the-art strategies such as adaptive query optimization and fine-grained partitioning approaches. Our enhanced methodology demonstrates how multiway joins can be efficiently implemented in a single MapReduce job by leveraging recent innovations in RDF storage and query processing. Experimental results validate the scalability and partial effectiveness of our proposed method on large-scale datasets, with notable improvements for specific query patterns over baseline approaches.